research-article

OCR-D: An end-to-end open source OCR framework for historical printed documents

Authors:

Clemens Neudecker,

Konstantin Baierer,

Maria Federbusch,

Matthias Boenig,

Kay-Michael Würzner,

Volker Hartmann,

Elisa HerrmannAuthors Info & Claims

DATeCH2019: Proceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage

Pages 53 - 58

https://doi.org/10.1145/3322905.3322917

Published: 08 May 2019 Publication History

Abstract

Various research projects were concerned with the development and adaptation of methods for OCR specifically for historical printed documents (cf. METAe [20], IMPACT [1], eMOP [9]). However, these initiatives have ended before the wide adoption of deep neural networks and, despite the various project's achievements, there remains a lack of OCR software that is a) comprehensive with regard to the challenges presented by the wide variety of historical documents and b) available as ready-to-use Free Software. The OCR-D project aims to rectify that.

In this paper we introduce the background of OCR-D, the main challenges and shortcomings in the availability of open tools and resources for OCR of historical printed documents and discuss the various software modules and related components (repositories, workflows) that are being made available through OCR-D. Finally we provide an outlook to a number of remaining challenges that are not addressed by OCR-D and point out several examples for the positive community aspects arisen through the creation and sharing of open resources for historical German OCR.

References

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Cited By

Lee WChoi K(2024)Boundary Gaussian Distance Loss Function for Enhancing Character Extraction from High-Resolution Scans of Ancient Metal-Type Printed BooksElectronics10.3390/electronics1310195713:10(1957)Online publication date: 16-May-2024
https://doi.org/10.3390/electronics13101957
Kaiser M(2023)Digitale Sammlungen als offene Daten für die ForschungBibliothek Forschung und Praxis10.1515/bfp-2023-002147:2(200-212)Online publication date: 18-Jul-2023
https://doi.org/10.1515/bfp-2023-0021
Neudecker C(2023)Digital Curation and AIAI in Museums10.14361/9783839467107-013(149-162)Online publication date: 4-Dec-2023
https://doi.org/10.14361/9783839467107-013
Show More Cited By

Index Terms

OCR-D: An end-to-end open source OCR framework for historical printed documents
1. Applied computing
  1. Document management and text processing
    1. Document capture
      1. Optical character recognition

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DATeCH2019: Proceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage

May 2019

163 pages

ISBN:9781450371940

DOI:10.1145/3322905

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 May 2019

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Deutsche Forschungsgemeinschaft

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DATeCH2019

DATeCH2019: 3rd International Conference on Digital Access to Textual Cultural Heritage

May 8 - 10, 2019

Brussels, Belgium

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Cited By

Lee WChoi K(2024)Boundary Gaussian Distance Loss Function for Enhancing Character Extraction from High-Resolution Scans of Ancient Metal-Type Printed BooksElectronics10.3390/electronics1310195713:10(1957)Online publication date: 16-May-2024
https://doi.org/10.3390/electronics13101957
Kaiser M(2023)Digitale Sammlungen als offene Daten für die ForschungBibliothek Forschung und Praxis10.1515/bfp-2023-002147:2(200-212)Online publication date: 18-Jul-2023
https://doi.org/10.1515/bfp-2023-0021
Neudecker C(2023)Digital Curation and AIAI in Museums10.14361/9783839467107-013(149-162)Online publication date: 4-Dec-2023
https://doi.org/10.14361/9783839467107-013
Rezanezhad VBaierer KGerber MLabusch KNeudecker C(2023)Document Layout Analysis with Deep Learning and HeuristicsProceedings of the 7th International Workshop on Historical Document Imaging and Processing10.1145/3604951.3605513(73-78)Online publication date: 25-Aug-2023
https://dl.acm.org/doi/10.1145/3604951.3605513
Banyasz DHofstätter SHanbury A(2023)Search in Archival Facsimile Documents for Digital History2023 IEEE 19th International Conference on e-Science (e-Science)10.1109/e-Science58273.2023.10254826(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/e-Science58273.2023.10254826
Nagane APatil CMali S(2023)Classification of Brahmi script characters using HOG features and multiclass error-correcting output codes (ECOC) model containing SVM binary learners2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE57236.2023.10091084(448-451)Online publication date: 27-Jan-2023
https://doi.org/10.1109/IITCEE57236.2023.10091084
Kordon FWeichselbaumer NHerz RMossman SPotten ESeuret MMayr MChristlein V(2023)Classification of incunable glyphs and out-of-distribution detection with joint energy-based modelsInternational Journal on Document Analysis and Recognition (IJDAR)10.1007/s10032-023-00442-x26:3(223-240)Online publication date: 22-Jun-2023
https://doi.org/10.1007/s10032-023-00442-x
Ströbel PHodel TBoente WVolk M(2023)The Adaptability of a Transformer-Based OCR Model for Historical DocumentsDocument Analysis and Recognition – ICDAR 2023 Workshops10.1007/978-3-031-41498-5_3(34-48)Online publication date: 15-Aug-2023
https://doi.org/10.1007/978-3-031-41498-5_3
Nicolaou ALuger DDecker FRenet NChristlein VVogeler G(2023)Efficient Annotation of Medieval ChartersDocument Analysis and Recognition – ICDAR 2023 Workshops10.1007/978-3-031-41498-5_20(284-295)Online publication date: 15-Aug-2023
https://doi.org/10.1007/978-3-031-41498-5_20
Găman MGhadamiyan LIonescu RPopescu M(2023)Self-paced Learning to Improve Text Row Detection in Historical Documents with Missing LabelsComputer Vision – ECCV 2022 Workshops10.1007/978-3-031-25069-9_17(253-262)Online publication date: 14-Feb-2023
https://doi.org/10.1007/978-3-031-25069-9_17
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